Many systems and methods intended for use by elderly people are known in the art. Elderly people as a group have less developed technological skills than younger generations. These people may also have various disabilities or degraded capabilities as compared to their youth. Further, elderly people tend to be retired, and thus do not spend their time focused on an avocation.
Speech recognition technologies, as described, for example in Gupta, U.S. Pat. No. 6,138,095, incorporated herein by reference, are programmed or trained to recognize the words that a person is saying. Various methods of implementing these speech recognition technologies include either associating the words spoken by a human with a dictionary lookup and error checker or through the use of neural networks which are trained to recognize words. See also:
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The current scholarly trend is to use statistical modeling to determine whether a sound is a phoneme, and whether a certain set of phonemes corresponds to a word. This method is discussed in detail in Turner, Statistical Methods for Natural Sounds (Thesis, University of London, 2010), incorporated herein by reference. Other scholars have applied Hidden Markov Models (HMM) to speech recognitions. Hidden Markov Models are probabilistic models that assume that at any given time, the system is in a state (e.g. uttering a first phoneme). In the next time-step, the system moves to another state with a certain probability (e.g. either uttering a second phoneme, completing a word, or completing a sentence). The model keeps track of the current state and attempts to determine the next state in accordance with a set of rules. See, generally, Brown, Decoding HMMs using the k best paths: algorithms and applications, BMC Bioinformatics (2010), incorporated herein by reference, for a more complete discussion of the application of HMMs.
In addition to recognizing the words that a human has spoken, speech recognition software can also be programmed to determine the mood of a speaker, or to determine basic information that is apparent from the speaker's voice, tone, and pronunciation, such as the speaker's gender, approximate age, accent, and language. See, for example, Bohacek, U.S. Pat. No. 6,411,687, incorporated herein by reference, describing an implementation of these technologies. See also, Leeper, Speech Fluency, Effect of Age, Gender and Context, International Journal of Phoniatrics, Speech Therapy and Communication Pathology (1995), incorporated herein by reference, discussing the relationship between the age of the speaker, the gender of the speaker, and the context of the speech, in the fluency and word choice of the speaker. In a similar field of endeavor, Taylor, U.S. Pat. No. 6,853,971, teaches an application of speech recognition technology to determine the speaker's accent or dialect. See also: U.S. App. 2007/0198261, U.S. App. 2003/0110038, and U.S. Pat. No. 6,442,519, all incorporated herein by reference.
In addition, a computer with a camera attached thereto can be programmed to recognize facial expressions and facial gestures in order to ascertain the mood of a human. See, for example, Black, U.S. Pat. No. 5,774,591, incorporated herein by reference. One implementation of Black's technique is by comparing facial images with a library of known facial images that represent certain moods or emotions. An alternative implementation would ascertain the facial expression through neural networks trained to do so. Similarly, Kodachi, U.S. Pat. No. 6,659,857, incorporated herein by reference, teaches about the use of a “facial expression determination table” in a gaming situation so that a user's emotions can be determined. See also U.S. Pat. No. 6,088,040, U.S. Pat. No. 7,624,076, U.S. Pat. No. 7,003,139, U.S. Pat. No. 6,681,032, and U.S. App. 2008/0101660.
Takeuchi, “Communicative Facial Displays as a New Conversational Modality,” (1993) incorporated herein by reference, notes that facial expressions themselves could be communicative. Takeuchi's study compared a group of people who heard a voice only and a group of people who viewed a face saying the same words as the voice. The people who saw the face had a better understanding of the message, suggesting a communicative element in human facial expressions. Catrambone, “Anthropomorphic Agents as a User Interface Paradigm: Exponential Findings and a Framework for Research,” incorporated herein by reference, similarly, notes that users who learn computing with a human face on the computer screen guiding them through the process feel more comfortable with the machines as a result.
Lester goes even further, noting that “animated pedagogical agents” can be used to show a face to students as a complex task is demonstrated on a video or computer screen. The computer (through the face and the speaker) can interact with the students through a dialog. Lester, “Animated Pedagogical Agents: Face-to-Face Interaction in Interactive Learning Environments,” North Carolina State University (1999), incorporated herein by reference. Cassell, similarly, teaches about conversational agents. Cassell's “embodied conversational agents” (ECAs) are computer interfaces that are represented by human or animal bodies and are lifelike or believable in their interaction with the human user. Cassell requires ECAs to have the following features: the ability to recognize and respond to verbal and nonverbal input; the ability to generate verbal and nonverbal output; the ability to deal with conversational functions such as turn taking, feedback, and repair mechanisms; and the ability to give signals that indicate the state of the conversation, as well as to contribute new propositions to the discourse. Cassell, “Conversation as a System Framework: Designing Embodied Conversational Agents,” incorporated herein by reference.
Massaro continues the work on conversation theory by developing Baldi, a computer animated talking head. When speaking, Baldi imitates the intonations and facial expressions of humans. Baldi has been used in language tutoring for children with hearing loss. Massaro, “Developing and Evaluating Conversational Agents,” Perpetual Science Laboratory, University of California. In later developments, Baldi was also given a body so as to allow for communicative gesturing and was taught to speak multiple languages. Massaro, “A Multilingual Embodied Conversational Agent,” University of California, Santa Cruz (2005), incorporated herein by reference.
Bickmore continues Cassell's work on embodied conversational agents. Bickmore finds that, in ECAs, the nonverbal channel is crucial for social dialogue because it is used to provide social cues, such as attentiveness, positive affect, and liking and attraction. Facial expressions also mark shifts into and out of social activities. Also, there are many gestures, e.g. waving one's hand to hail a taxi, crossing ones arms and shaking ones head to say “No,” etc. that are essentially communicative in nature and could serve as substitutes for words.
Bickmore further developed a computerized real estate agent, Rea, where, “Rea has a fully articulated graphical body, can sense the user passively through cameras and audio input, and is capable of speech with intonation, facial display, and gestural output. The system currently consists of a large projection screen on which Rea is displayed and which the user stands in front of. Two cameras mounted on top of the projection screen track the user's head and hand positions in space. Users wear a microphone for capturing speech input.” Bickmore & Cassell, “Social Dialogue with Embodied Conversational Agents,” incorporated herein by reference.
Similar to the work of Bickmore and Cassell, Beskow at the Royal Institute of Technology in Stockholm, Sweden created Olga, a conversational agent with gestures that is able to engage in conversations with users, interpret gestures, and make its own gestures. Beskow, “Olga—A Conversational Agent with Gestures,” Royal Institute of Technology, incorporated herein by reference.
In “Social Cues in Animated Conversational Agents,” Louwerse et al note that people who interact with ECAs tend to react to them just as they do to real people. People tend to follow traditional social rules and to express their personality in usual ways in conversations with computer-based agents. Louwerse, M. M., Graesser, A. C., Lu, S., & Mitchell, H. H. (2005). Social cues in animated conversational agents. Applied Cognitive Psychology, 19, 1-12, incorporated herein by reference.
In another paper, Beskow further teaches how to model the dynamics of articulation for a parameterized talking head based on phonetic input. Beskow creates four models of articulation (and the corresponding facial movements). To achieve this result, Beskow makes use of neural networks. Beskow further notes several uses of “talking heads”. These include virtual language tutors, embodied conversational agents in spoken dialogue systems, and talking computer game characters. In the computer game area, proper visual speech movements are essential for the realism of the characters. (This factor also causes “dubbed” foreign films to appear unrealistic.) Beskow, “Trainable Articulatory Control Models for Visual Speech Synthesis” (2004), incorporated herein by reference.
Ezzat goes even further, presenting a technique where a human subject is recorded uttering a predetermined speech corpus by video camera. A visual speech model is created from this recording. Now, the computer can allow the person to make novel utterances and show how she would move her head while doing so. Ezzat creates a “multidimensional morpheme model” to synthesize new, previously unseen mouth configurations from a small set of mouth image prototypes.
In a similar field of endeavor, Picard proposes computer that can respond to user's emotions. Picard's ECAs can be used as an experimental emotional aid, as a pre-emptive tool to avert user frustration, and as an emotional skill-building mirror.
In the context of a customer call center, Bushey, U.S. Pat. No. 7,224,790, incorporated herein by reference, discusses conducting a “verbal style analysis” to determine a customer's level of frustration and the customer's goals in calling customer service. The “verbal style analysis” takes into account the number of words that the customer uses and the method of contact. Based in part on the verbal style analysis, customers are segregated into behavioral groups, and each behavioral group is treated differently by the customer service representatives. Gong, U.S. App. 2003/0187660, incorporated herein by reference, goes further than Bushey, teaching an “intelligent social agent” that receives a plurality of physiological data and forms a hypothesis regarding the “affective state of the user” based on this data. Gong also analyzes vocal and verbal content, and integrates the analysis to ascertain the user's physiological state.
Mood can be determined by various biometrics. For example, the tone of a voice or music is suggestive of the mood. See, Liu et al, Automatic Mood Detection from Acoustic Music Data, Johns Hopkins University Scholarship Library (2003). The mood can also be ascertained based on a person's statements. For example, if a person says, “I am angry,” then the person is most likely telling the truth. See Kent et al, Detection of Major and Minor Depression in Children and Adolescents, Journal of Child Psychology (2006). One's facial expression is another strong indicator of one's mood. See, e.g., Cloud, How to Lift Your Mood? Try Smiling. Time Magazine (Jan. 16, 2009).
Therefore, it is feasible for a human user to convey his mood to a machine with an audio and a visual input by speaking to the machine, thereby allowing the machine to read his voice tone and words, and by looking at the machine, thereby allowing the machine to read his facial expressions.
It is also possible to change a person's mood through a conversational interface. For example, when people around one are smiling and laughing, one is more likely to forget one's worries and to smile and laugh oneself. In order to change a person's mood through a conversational interface, the machine implementing the interface must first determine the starting mood of the user. The machine would then go through a series of “optimal transitions” seeking to change the mood of the user. This might not be a direct transition. Various theories discuss how a person's mood might be changed by people or other external influences. For example Neumann, “Mood Contagion”: The Automatic Transfer of Mood Between persons, Journal of Personality and Social Psychology (2000), suggests that if people around one are openly experiencing a certain mood, one is likely to join them in experiencing said mood. Other scholars suggest that logical mood mediation might be used to persuade someone to be happy. See, e.g., DeLongis, The Impact of Daily Stress on Health and Mood: Psychological and Social Resources as Mediators, Journal of Personality and Social Psychology (1988). Schwarz notes that mood can be impacted by presenting stimuli that were previously associated with certain moods, e.g. the presentation of chocolate makes one happy because one was previously happy when one had chocolate. Schwarz, Mood and Persuasion: Affective States Influence the Processing of Persuasive Communications, in Advances in Experimental Social Psychology, Vol. 24 (Academic Press 1991). Time Magazine suggests that one can improve one's mood merely by smiling or changing one's facial expression to imitate the mood one wants to experience. Cloud, How to Lift Your Mood? Try Smiling. Time Magazine (Jan. 16, 2009).
Liquid crystal display (LCD) screens are known in the art as well. An LCD screen is a thin, flat electronic visual display that uses the light modulating properties of liquid crystals. These are used in cell phones, smart phones, laptops, desktops, and televisions. See Huang, U.S. Pat. No. 6,437,975, incorporated herein by reference, for a detailed discussion of LCD screen technology.
Many other displays are known in the art. For example, three dimensional televisions and monitors are available from Samsung Corp. and Philips Corp. One embodiment of the operation of three dimensional television, described by Imsand in U.S. Pat. No. 4,723,159, involves taking two cameras and applying mathematical transforms to combine the two received images of an object into a single image, which can be displayed to a viewer. On its website, Samsung notes that its three dimensional televisions operate by “display[ing] two separate but overlapping images of the same scene simultaneously, and at slightly different angles as well.” One of the images is intended to be perceived by the viewer's left eye. The other is intended to be perceived by the right eye. The human brain should convert the combination of the views into a three dimensional image. See, generally, Samsung 3D Learning Resource, www.samsung.com/us/learningresources3D (last accessed May 10, 2010).
Projectors are also known in the art. These devices project an image from one screen to another. Thus, for example, a small image on a cellular phone screen that is difficult for an elderly person to perceive may be displayed as a larger image on a wall by connecting the cell phone with a projector. Similarly, a netbook with a small screen may be connected by a cable to a large plasma television or plasma screen. This would allow the images from the netbook to be displayed on the plasma display device.
Devices for forming alternative facial expressions are known in the art. There are many children's toys and pictures with changeable facial expressions. For example, Freynet, U.S. Pat. No. 6,146,721, incorporated herein by reference, teaches a toy having alternative facial expression. An image of a face stored on a computer can be similarly presented on a LCD screen with a modified facial expression. See also U.S. Pat. No. 5,215,493, U.S. Pat. No. 5,902,169, U.S. Pat. No. 3,494,068, and U.S. Pat. No. 6,758,717, expressly incorporated herein by reference.
In addition, emergency detection systems taking input from cameras and microphones are known in the art. These systems are programmed to detect whether an emergency is ongoing and to immediately notify the relevant parties (e.g. police, ambulance, hospital or nursing home staff, etc.). One such emergency detection system is described by Lee, U.S. Pat. No. 6,456,695, expressly incorporated herein by reference. Lee suggests that an emergency call could be made when an emergency is detected, but does not explain how an automatic emergency detection would take place. However, Kirkor, U.S. Pat. No. 4,319,229, proposes a fire emergency detector comprising “three separate and diverse sensors . . . a heat detector, a smoke detector, and an infrared radiation detector.” Under Kirkor's invention, when a fire emergency is detected, (through the combination of inputs to the sensors) an alarm is sounded to alert individuals in the building and the local fire department is notified via PSTN. In addition, some modern devices, for example the Emfit Movement Monitor/Nighttime Motion Detection System, www.gosouthernmd.com/store/store/comersus_viewItem.asp?idProduct=35511, last accessed May 10, 2010, comprise a camera and a pressure sensor adapted to watch a sleeping person and to alert a caregiver when the sleeping patient is exhibiting unusual movements.
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